Data-driven modal surrogate model for frequency response uncertainty propagation
Artikel i vetenskaplig tidskrift, 2021

A method is developed for propagation of model parameter uncertainties into frequency response functions based on a modal representation of the equations of motion. Individual local surrogate models of the eigenfrequencies and residue matrix elements for each mode are trained to build a global surrogate model. The computational cost of the global surrogate model is reduced in three steps. First, modes outside the range of interest, necessary to describe the in-band frequency response, are approximated with few residual modes. Secondly, the dimension of the residue matrices for each mode is reduced using principal component analysis. Lastly, multiple surrogate model structures are employed in a mixture. Cheap second-order multivariate polynomial models and more expensive Gaussian process models with different kernels are used to model the modal data. Leave-one-out cross-validation is used for model selection of the local surrogate models. The approximations introduced allow the method to be used for modally dense models at a small computational cost, without sacrificing the global surrogate model's ability to capture mode veering and crossing phenomena. The method is compared to a Monte Carlo based approach and verified on one industrial-sized component and on one assembly of two car components.

Frequency response functions

Uncertainty propagation

Principal component analysis

Gaussian process

Multivariate polynomial

Monte Carlo method

Författare

Mladen Gibanica

Volvo

Chalmers, Mekanik och maritima vetenskaper, Dynamik

Thomas Abrahamsson

Chalmers, Mekanik och maritima vetenskaper, Dynamik

Probabilistic Engineering Mechanics

0266-8920 (ISSN)

Vol. 66 103142

Ämneskategorier

Teknisk mekanik

Sannolikhetsteori och statistik

Reglerteknik

DOI

10.1016/j.probengmech.2021.103142

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Senast uppdaterat

2021-07-15